Development of Water Quality Analysis for Anomaly Detection and Correlation with Case Studies in Water Supply Systems

The increasing importance of water quality management in water supply systems requires the development of efficient methodologies for the early detection of water quality incidents related to the detection of anomalies in water quality parameters. Research aims to analyze real-time water quality data (pH, turbidity, electrical conductivity, temperature, and chlorine), perform anomaly detection across parameters, and conduct a comprehensive investigation of water quality incidents that correlate with detected anomalies in water supply systems. This study can contribute to the development of an early detection and response system related to water quality incidents in water supply systems. Future work will focus on enhancing the application of systems for early detection of water quality incidents by expanding the data, developing anomaly detection methods by applying machine learning techniques, and figuring out the correlations between anomalies and water quality incidents.